A Root-n Consistent Backfitting Estimator for Semiparametric Additive Modeling
A Root-n Consistent Backfitting Estimator for Semiparametric Additive Modeling
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DOI：
10.1080/10618600.1999.10474845
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发表时间：
1999-12
影响因子：
2.4
通讯作者：
J. Opsomer;D. Ruppert
中科院分区：
文献类型：
--
作者：
J. Opsomer;D. Ruppert
摘要我们研究了同时包含参数项和非参数项的加性模型，并给出了模型参数分量的√n-相容反拟估计。对于具有单个非参数项的情形，给出了估计量的理论性质，并将其推广到任意数目的非参数加性项。提出了一种最优带宽估计器，该估计器最大限度地利用了偏差和方差的渐近表达式，并给出了模型拟合和带宽选择的快速实现算法。通过仿真实验说明了该估计器和带宽选择的实际行为。
Abstract We explore additive models that combine both parametric and nonparametric terms and propose a √n-consistent backfitting estimator for the parametric component of the model. The theoretical properties of the estimator are developed for the case with a single nonparametric term and extended to an arbitrary number of nonparametric additive terms. An estimator for the optimal bandwidth making minimal use of asymptotic expressions for bias and variance is proposed, and a fast implementation algorithm for model fitting and bandwidth selection is developed. The practical behavior of the estimator and bandwidth selection is illustrated by simulation experiments.